Simpson’s Paradox: Aggregation Effects in Statistical and Machine Learning Models

Data aggregation effects are examined in relation to both statistical and machine learning approaches to data modeling. It is shown that heavily data-centric artificial neural network and random forest methods are subject to aggregation effects similar to those affecting statistical methods. Several basic examples are discussed.